Big data described as bulk information. Hadoop is an open-source, Java-based programming framework that supports the processing and storage of Big Data. A computer cluster is a set of connected computers that can work together as a single system. Hadoop Clusters are such type of computer clusters that can store, analyse big data which are structured and unstructured. Azure HDInsight deploys these Azure Hadoop clusters in the cloud using the Hortonworks Data Platform (HDP) Hadoop distribution. It also consists of Apache HBase which is a tabular NoSQL database that provides a real-time access to data in HDFS. Apache Storm is a stream analytics platform for processing real-time events like sensors.
It is a general-purpose storage system connected to compute nodes. By storing the data in Azure Storage one has the benefits of data sharing, data achieving, geo-replication and elastic scaling capabilities. These enable data recovery and redundancy. The scale-out file system automatically scaled depending upon a number of nodes connected to the cluster. Every time when a cluster is generated, there is no need to reload the data. Even after the original HDInsight cluster is deleted, you can still use the default storage container.
Let us consider a healthcare monitoring development and operational cycle.
The above is a health care monitoring process that happens in any hospital. Using Azure HDInsights, you can have time-to-time monitoring on each process, the status of servers and finally depicts faults and errors if occurred. Azure Insight is deployed in the healthcare product.
After registering the hospital application in the Azure portal and when you start running it, you get the overall performance of healthcare application as given in the below figure.
Fig: Overall Application Performance Metrics
It shows Browser metrics like page views, page load time, request on each page, each session etc
The failures and errors occurred while performing a task in the application like server exceptions, page faults, data dependency failures etc
Fig: Failures Metrics
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